{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T03:14:58Z","timestamp":1781925298957,"version":"3.54.5"},"reference-count":30,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,1,9]],"date-time":"2020-01-09T00:00:00Z","timestamp":1578528000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000203","name":"U.S. Geological Survey","doi-asserted-by":"publisher","award":["G17AC00070"],"award-info":[{"award-number":["G17AC00070"]}],"id":[{"id":"10.13039\/100000203","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The split window technique has been used for over thirty years to derive surface temperatures of the Earth with image data collected from spaceborne sensors containing two thermal channels. The latest NASA\/USGS Landsat satellites contain the Thermal Infrared Sensor (TIRS) instruments that acquire Earth data in two longwave infrared bands, as opposed to a single band with earlier Landsats. The United States Geological Survey (USGS) will soon begin releasing a surface temperature product for Landsats 4 through 8 based on the single spectral channel methodology. However, progress is being made toward developing and validating a more accurate and less computationally intensive surface temperature product based on the split window method for Landsat 8 and 9 datasets. This work presents the progress made towards developing an operational split window algorithm for TIRS. Specifically, details of how the generalized split window algorithm was tailored for the TIRS sensors are presented, along with geometric considerations that should be addressed to avoid spatial artifacts in the final surface temperature product. Validation studies indicate that the proposed algorithm is accurate to within 2 K when compared to land-based measurements and to within 1 K when compared to water-based measurements, highlighting the improved accuracy that may be achieved over the current single-channel methodology being used to derive surface temperature in the Landsat Collection 2 surface temperature product. Surface temperature products using the split window methodologies described here can be made available upon request for testing purposes.<\/jats:p>","DOI":"10.3390\/rs12020224","type":"journal-article","created":{"date-parts":[[2020,1,10]],"date-time":"2020-01-10T04:06:51Z","timestamp":1578629211000},"page":"224","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Towards an Operational, Split Window-Derived Surface Temperature Product for the Thermal Infrared Sensors Onboard Landsat 8 and 9"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7158-4830","authenticated-orcid":false,"given":"Aaron","family":"Gerace","sequence":"first","affiliation":[{"name":"Rochester Institute of Technology, 54 Lomb Memorial Drive, Rochester, NY 14624, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tania","family":"Kleynhans","sequence":"additional","affiliation":[{"name":"Rochester Institute of Technology, 54 Lomb Memorial Drive, Rochester, NY 14624, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rehman","family":"Eon","sequence":"additional","affiliation":[{"name":"Rochester Institute of Technology, 54 Lomb Memorial Drive, Rochester, NY 14624, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew","family":"Montanaro","sequence":"additional","affiliation":[{"name":"Rochester Institute of Technology, 54 Lomb Memorial Drive, Rochester, NY 14624, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5717","DOI":"10.1109\/TGRS.2018.2824828","article-title":"An Operational Land Surface Temperature Product for Landsat Thermal Data: Methodology and Validation","volume":"56","author":"Malakar","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1016\/j.rse.2018.06.026","article-title":"Uncertainty estimation method and Landsat 7 global validation for the Landsat surface temperature product","volume":"216","author":"Laraby","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.3390\/rs70101135","article-title":"The Thermal Infrared Sensor (TIRS) on Landsat 8: Design Overview and Pre-Launch Characterization","volume":"7","author":"Reuter","year":"2015","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.rse.2017.01.029","article-title":"Derivation and validation of the stray light correction algorithm for the Thermal Infrared Sensor onboard Landsat 8","volume":"191","author":"Gerace","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"11153","DOI":"10.3390\/rs61111153","article-title":"Landsat 8 Thermal Infrared Sensor Geometric Characterization and Calibration","volume":"6","author":"Storey","year":"2014","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1080\/01431169008955028","article-title":"Towards a local split window method over land surfaces","volume":"11","author":"Becker","year":"1990","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1109\/36.508406","article-title":"A generalized split-window algorithm for retrieving land-surface temperature from space","volume":"34","author":"Wan","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.rse.2006.06.026","article-title":"New Refinements and Validation of the MODIS Land-Surface Temperature\/Emissivity Products","volume":"140","author":"Wan","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Berk, A., Conforti, P., Kennett, R., Perkins, T., Hawes, F., and van den Bosch, J. (2014, January 24\u201327). MODTRAN6: A major upgrade of the MODTRAN radiative transfer code. Proceedings of the 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Lausanne, Switzerland.","DOI":"10.1109\/WHISPERS.2014.8077573"},{"key":"ref_10","unstructured":"(2019, October 22). Thermodynamic Initial Guess Retrieval (TIGR). Available online: http:\/\/ara.abct.lmd.polytechnique.fr\/index.php?page=tigr."},{"key":"ref_11","unstructured":"(2019, October 22). MODIS UCSB Emissivity Library. Available online: https:\/\/icess.eri.ucsb.edu\/modis\/EMIS\/html\/em.html."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"647","DOI":"10.3390\/rs70100647","article-title":"A Practical Split-Window Algorithm for Estimating Land Surface Temperature from Landsat 8 Data","volume":"7","author":"Du","year":"2015","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"7966","DOI":"10.1002\/2015GL065564","article-title":"The ASTER Global Emissivity Dataset (ASTER GED): Mapping Earth\u2019s emissivity at 100 meter spatial scale","volume":"42","author":"Hulley","year":"2015","journal-title":"Geophys. Res. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"197","DOI":"10.4314\/sajg.v3i2.6","article-title":"A comparison of Normalised Difference Snow Index (NDSI) and Normalised Difference Principal Component Snow Index (NDPCSI) techniques in distinguishing snow from related land cover types","volume":"3","author":"Sibandze","year":"2014","journal-title":"S. Afr. J. Geomat."},{"key":"ref_15","unstructured":"(2019, October 22). SURFRAD (Surface Radiation Budget) Network, Available online: https:\/\/www.esrl.noaa.gov\/gmd\/grad\/surfrad\/."},{"key":"ref_16","unstructured":"(2019, October 22). AmeriFlux Network, Available online: https:\/\/ameriflux.lbl.gov\/."},{"key":"ref_17","unstructured":"(2019, October 22). National Data Buoy Center, Available online: https:\/\/www.ndbc.noaa.gov\/."},{"key":"ref_18","unstructured":"(2019, October 22). Lake Tahoe Validation, Available online: https:\/\/laketahoe.jpl.nasa.gov\/get_met_weather."},{"key":"ref_19","unstructured":"(2019, October 22). Salton Sea Validation, Available online: https:\/\/saltonsea.jpl.nasa.gov\/get_met_weather."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1002\/rog.20009","article-title":"Global atmospheric downward longwave radiation at the surface from ground-based observations, satellite retrievals, and reanalyses","volume":"51","author":"Wang","year":"2013","journal-title":"Rev. Geophys."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.rse.2012.05.004","article-title":"Land Surface Temperature product validation using NOAA\u2019s surface climate observation networks\u2014Scaling methodology for the Visible Infrared Imager Radiometer Suite (VIIRS)","volume":"124","author":"Guillevic","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_22","unstructured":"(2019, October 22). University of Michigan Biological Station. Available online: http:\/\/flux.org.ohio-state.edu\/site-description-umbs\/#umbs-ameriflux-towers."},{"key":"ref_23","unstructured":"Gr\u00f6bner, J., and Wacker, S. (2020, January 08). Pyrgeometer Calibration Procedure at the PMOD\/WRC-IRS. Available online: https:\/\/library.wmo.int\/doc_num.php?explnum_id=7365."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, K., Wan, Z., Wang, P., Sparrow, M., Liu, J., Zhou, X., and Haginoya, S. (2005). Estimation of surface long wave radiation and broadband emissivity using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature\/emissivity products. J. Geophys. Res. Atmos., 110.","DOI":"10.1029\/2004JD005566"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/0034-4257(88)90032-6","article-title":"Emissivity of pure and sea waters for the model sea surface in the infrared window regions","volume":"24","author":"Masuda","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1029\/1998JC900060","article-title":"A multiyear hourly sea surface skin temperature data set derived from the TOGA TAO bulk temperature and wind speed over the tropical Pacific","volume":"104","author":"Zeng","year":"1999","journal-title":"J. Geophys. Res. Oceans"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1962","DOI":"10.3390\/rs70201962","article-title":"Pre- and Post-Launch Spatial Quality of the Landsat 8 Thermal Infrared Sensor","volume":"7","author":"Wenny","year":"2015","journal-title":"Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1109\/TGRS.2011.2162338","article-title":"Validation of GOES-R Satellite Land Surface Temperature Algorithm Using SURFRAD Ground Measurements and Statistical Estimates of Error Properties","volume":"50","author":"Yu","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1080\/01431161.2013.873149","article-title":"Evaluation of 10 year AQUA\/MODIS land surface temperature with SURFRAD observations","volume":"35","author":"Li","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"12215","DOI":"10.3390\/rs70912215","article-title":"Quality assessment of S-NPP VIIRS land surface temperature product","volume":"7","author":"Liu","year":"2015","journal-title":"Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/2\/224\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:04:19Z","timestamp":1760364259000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/2\/224"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,9]]},"references-count":30,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["rs12020224"],"URL":"https:\/\/doi.org\/10.3390\/rs12020224","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,9]]}}}